def Main(outfile = "testPiecewiseConstantDiffusion", grid_points = tuple([2000]), L_max = 3, algo_name = "whtp", gamma = 1.035, abar = 5, variability = None, L_min = 1, sampling_name = "p", nb_iter = 50, epsilon = 1e-4, nb_tests = None, dat_constant = 10):
## SPDEModel
d = 13
# epsilon = 50 # Is used for the number of iterations in whtp
# mesh_size = int(d*math.floor(float(grid_points)/float(d)))
# Create FEMModel with given diffusion coefficient, goal functional and initial mesh size
if variability is None:
variability = abar/(d+1)
#upper_bound = abar + variability
#a = [LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound),
# LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound),
# LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound)] # Make sure you have d of those
spde_model = PiecewiseConstantDiffusionFEMModelML(abar, ConstantCoefficient(1.0), variability, grid_points, Average())
test_result = outfile, None
for s in range(L_min,L_max+1,1):
## Reconstruction Model
v = [gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, gamma, np.inf] # This has to be done better too
wr_model = WR.WRModel(WR.Algorithms.whtp, WR.Operators.Chebyshev, v,
get_sampling_type(sampling_name), WR.check_cs)
num_tests = nb_tests # change from 10 for Quinoa tests
## Don't forget to reset the original mesh
spde_model.refine_mesh(2**(-s))
### Execute test
test_result = test(spde_model, wr_model, nb_iter, epsilon, s, [CrossCheck(num_tests)], dat_constant, *test_result)
def Main(outfile = "testPiecewiseConstantDiffusionPoly", grid_points = 2000, L_max = 3, algo_name = "whtp", c = 1, alpha = 1/2, abar = 5, variability = None, L_min = 1, sampling_name = "p", nb_tests = None):
if algo_name == 'whtp': # Really have to find a way to deal with the epsilon/eta/nbIter parameter
epsilon = 1e-4 # This will be rescaled later
elif algo_name == 'wiht':
epsilon = 1e-4
elif algo_name == 'womp':
epsilon = 1e-4
elif algo_name == 'bpdn':
epsilon = 1e-4
else:
epsilon = 1e-4 # This will be rescaled later
## SPDEModel
d = 13
# epsilon = 50 # Is used for the number of iterations in whtp
mesh_size = int(d*math.floor(float(grid_points)/float(d)))
# Create FEMModel with given diffusion coefficient, goal functional and initial mesh size
if variability is None:
variability = abar/(d+1)
#upper_bound = abar + variability
#a = [LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound),
# LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound),
# LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound), LinearCoefficient(abar, upper_bound)] # Make sure you have d of those
spde_model = PiecewiseConstantDiffusionFEMModelML(abar, ConstantCoefficient(1.0), variability, mesh_size, Average())
test_result = outfile, None
for s in range(L_min,L_max+1,1):
## Reconstruction Model
v = np.hstack((c*np.power([val+1 for val in range(d)], alpha), [np.inf]))
wr_model = WR.WRModel(WR.Algorithms.whtp, WR.Operators.Chebyshev, v,
get_sampling_type(sampling_name), WR.check_cs)
num_tests = nb_tests # change from 10 for Quinoa tests
## Don't forget to reset the original mesh
spde_model.refine_mesh(2**(-s))
### Execute test
test_result = test(spde_model, wr_model, epsilon, s, [CrossCheck(num_tests)], *test_result)
